Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-441-2024
https://doi.org/10.5194/amt-17-441-2024
Research article
 | 
23 Jan 2024
Research article |  | 23 Jan 2024

Real-time pollen identification using holographic imaging and fluorescence measurements

Sophie Erb, Elias Graf, Yanick Zeder, Simone Lionetti, Alexis Berne, Bernard Clot, Gian Lieberherr, Fiona Tummon, Pascal Wullschleger, and Benoît Crouzy

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1572', Anonymous Referee #1, 04 Sep 2023
    • AC1: 'Reply on RC1', Sophie Erb, 13 Nov 2023
  • RC2: 'Comment on egusphere-2023-1572', Anonymous Referee #2, 16 Oct 2023
    • AC2: 'Reply on RC2', Sophie Erb, 13 Nov 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Sophie Erb on behalf of the Authors (13 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (30 Nov 2023) by Rebecca Washenfelder
AR by Sophie Erb on behalf of the Authors (30 Nov 2023)  Author's response   Manuscript 
Download
Short summary
In this study, we focus on an automatic bioaerosol measurement instrument and investigate the impact of using its fluorescence measurement for pollen identification. The fluorescence signal is used together with a pair of images from the same instrument to identify single pollen grains via neural networks. We test whether considering fluorescence as a supplementary input improves the pollen identification performance by comparing three different neural networks.